Designing Eco-Friendly Kambuik Shopping Bags: A Quality Function Deployment Approach
Bibliographic record
Abstract
The proliferation of plastic shopping bags has raised concerns regarding environmental sustainability. To address this issue, technological advancements have enabled the development of eco-friendly shopping bags. These bags can be returned, contributing to a more eco-conscious shopping experience. However, aligning these bags with customer preferences and needs presents a significant challenge. To overcome this, we propose the use of the Quality Function Deployment (QFD) method. Applying the QFD method, we have identified the technical characteristics of eco-friendly shopping bags, utilizing woven Mansiang leaf material. The resulting bags measure 55 cm in length, 35 cm in height, and 8 cm in thickness. Furthermore, we present innovative designs, including bags, wallets, and flower holders. The inherent strength of the woven Kambuik material allows for efficient carrying capacity, offering a viable alternative to plastic shopping bags. Notably, Kambuik is derived from plants and has minimal environmental impact. Our research confirms the feasibility of designing safe and ecofriendly Mansiang leaf shopping bags, aligning with sustainable practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".